Using Deep Neural Networks To Classify Coral Reef And Algae
School Name
South Carolina Governor's School for Science & Mathematics
Grade Level
12th Grade
Presentation Topic
Computer Science
Presentation Type
Mentored
Written Paper Award
1st Place
Abstract
The human brain is easily the most complex structure in the known universe. Its processing power completely dwarfs the capabilities of the modern computer and most likely will for a long time. Even with all that processing power, however, computers are still far more accurate than humans. For this reason, the study of Machine Learning has risen dramatically. Machine Learning is the study of creating advance networks of mathematical computations that allow computers to “learn” how to classify and identify different problems, such as classifying dogs vs cats with high levels of accuracy. This lab is using the studies from Machine Learning to create a Convolutional Neural Network to process images of coral reefs that are feed to it through an aquatic robot out in the field. The computer can, with minimal effort, identify the different types of coral reefs with an accuracy of around 40%. The difficulty of this experiment is in the fine tuning of the Neural Network to allow an accuracy of around 95% and higher. Over the summer, we spent our time creating such a network that could classify coral reef. This model, if it were to be completed, would have been able to be used in tandem with the aquatic robots so that they could use the coral reef as landmarks to create virtual maps of underwater areas. They would have been able to use the virtual maps to plot out paths for navigation autonomously rather than human plotting paths for them.
Recommended Citation
Seekings, James, "Using Deep Neural Networks To Classify Coral Reef And Algae" (2019). South Carolina Junior Academy of Science. 306.
https://scholarexchange.furman.edu/scjas/2019/all/306
Location
Founders Hall 140 A
Start Date
3-30-2019 11:30 AM
Presentation Format
Oral Only
Group Project
No
Using Deep Neural Networks To Classify Coral Reef And Algae
Founders Hall 140 A
The human brain is easily the most complex structure in the known universe. Its processing power completely dwarfs the capabilities of the modern computer and most likely will for a long time. Even with all that processing power, however, computers are still far more accurate than humans. For this reason, the study of Machine Learning has risen dramatically. Machine Learning is the study of creating advance networks of mathematical computations that allow computers to “learn” how to classify and identify different problems, such as classifying dogs vs cats with high levels of accuracy. This lab is using the studies from Machine Learning to create a Convolutional Neural Network to process images of coral reefs that are feed to it through an aquatic robot out in the field. The computer can, with minimal effort, identify the different types of coral reefs with an accuracy of around 40%. The difficulty of this experiment is in the fine tuning of the Neural Network to allow an accuracy of around 95% and higher. Over the summer, we spent our time creating such a network that could classify coral reef. This model, if it were to be completed, would have been able to be used in tandem with the aquatic robots so that they could use the coral reef as landmarks to create virtual maps of underwater areas. They would have been able to use the virtual maps to plot out paths for navigation autonomously rather than human plotting paths for them.
